Segmentation-based image processing for confluency estimation
A method of determining a coverage of an image by an apparatus including processing circuitry includes executing, by the processing circuitry, instructions that cause the apparatus to generate a first segmentation mask by segmenting an image, generate a modified mask by applying a morphological operation to the first segmentation mask, generate a modified masked input based on the image and an inversion of the modified mask, generate a second segmentation mask by segmenting the modified masked input, and determine a coverage of the image based on the first segmentation mask and the second segmentation mask.
1. A method of operating an apparatus including processing circuitry, the method comprising:
executing, by the processing circuitry, instructions that cause the apparatus to:
1) generate a first segmentation mask by segmenting an image, generate a modified mask by applying a morphological operation to the first segmentation mask, generate a modified masked input based on the image and an inversion of the modified mask, and generate a second segmentation mask by segmenting the modified masked input;
2) combine the first segmentation mask and the second segmentation mask to generate a composite mask, wherein generating the composite mask includes applying a non-cell filter to the composite mask to exclude non-cell areas of the composite mask, and wherein excluding the non-cell areas of the composite mask is based on at least one of a morphology of the non-cell areas and a size of the non-cell areas; and
3) determine a coverage of the image based on the first segmentation mask and the second segmentation mask, wherein determining the coverage of the image includes a) presenting the image masked by the composite mask as an illustration of the coverage of the image and b) estimating a coverage of the composite mask.
2. The method of claim 1 , wherein
executing the instructions further causes the apparatus to adjust a feature of the image to generate an adjusted image, and
generating the first segmentation mask includes segmenting the adjusted image.
3. The method of claim 2 , wherein adjusting the feature of the image includes normalizing an illumination level of the image.
4. The method of claim 3 , wherein normalizing the illumination level of the image includes applying a Gaussian blur to the image to produce a blurred image and subtracting the blurred image from the image.
5. The method of claim 2 , wherein adjusting the feature of the image includes increasing a local contrast level of the image.
6. The method of claim 5 , wherein increasing the local contrast level of the image includes applying a contrast-limited adaptive histogram equalization to the image.
7. The method of claim 1 , wherein generating the first segmentation mask includes segmenting the image based on an edge filter.
8. The method of claim 1 , wherein generating the first segmentation mask includes applying a Gaussian blur to the image.
9. The method of claim 1 , wherein generating the first segmentation mask includes increasing a contrast level of the image.
10. The method of claim 1 , wherein the morphological operation includes at least one of:
an open morphological operation,
a close morphological operation,
a dilation morphological operation, and
an erosion morphological operation.
11. The method of claim 1 , wherein segmenting the modified masked input includes segmenting the modified masked input based on an edge filter.
12. The method of claim 1 , wherein segmenting the modified masked input includes applying a Gaussian blur to the modified masked input.
13. The method of claim 1 , wherein segmenting the modified masked input includes increasing a contrast level of the modified masked input.